Christian Tesche

Myocardial perfusion imaging with dual energy CT

Dual-energy CT (DECT) enables simultaneous use of two different tube voltages, thus different x-ray absorption characteristics are acquired in the same anatomic location with two different X-ray spectra. The various DECT techniques allow material decomposition and mapping of the iodine distribution within the myocardium. Static dual-energy myocardial perfusion imaging (sCTMPI) using pharmacological stress agents demonstrate myocardial ischemia by single snapshot images of myocardial iodine distribution. sCTMPI gives incremental values to coronary artery stenosis detected on coronary CT angiography (CCTA) by showing consequent reversible or fixed myocardial perfusion defects. The comprehensive acquisition of CCTA and sCTMPI offers extensive morphological and functional evaluation of coronary artery disease. Recent studies have revealed that dual-energy sCTMPI shows promising diagnostic accuracy for the detection of hemodynamically significant coronary artery disease compared to single-photon emission computed tomography, invasive coronary angiography, and cardiac MRI. The aim of this review is to present currently available DECT techniques for static myocardial perfusion imaging and recent clinical applications and ongoing investigations.

Dynamic CT myocardial perfusion imaging

Non-invasive cardiac imaging has rapidly evolved during the last decade due to advancements in CT based technologies. Coronary CT angiography has been shown to reliably assess coronary anatomy and detect high risk coronary artery disease. However, this technique is limited to anatomical assessment, thus non-invasive techniques for functional assessment of the heart are necessary. CT myocardial perfusion is a new CT based technique that provides functional assessment of the myocardium and allows for a comprehensive assessment of coronary artery disease with a single modality when combined with CTA. This review aims to discuss dynamic CT myocardial perfusion as a new technique in the assessment of CAD.

Coronary CT Angiography–derived Fractional Flow Reserve

Invasive coronary angiography (ICA) with measurement of fractional flow reserve (FFR) by means of a pressure wire technique is the established reference standard for the functional assessment of coronary artery disease (CAD) ( 1 , 2 ). Coronary computed tomographic (CT) angiography has emerged as a noninvasive method for direct assessment of CAD and plaque characterization with high diagnostic accuracy compared with ICA ( 3 , 4 ). However, the solely anatomic assessment provided with both coronary CT angiography and ICA has poor discriminatory power for ischemia-inducing lesions. FFR derived from standard coronary CT angiography (FFRCT) data sets by using any of several advanced computational analytic approaches enables combined anatomic and hemodynamic assessment of a coronary lesion by a single noninvasive test. Current technical approaches to the calculation of FFRCT include algorithms based on full- and reduced-order computational fluid dynamic modeling, as well as artificial intelligence deep machine learning ( 5 , 6 ). A growing body of evidence has validated the diagnostic accuracy of FFRCT techniques compared with invasive FFR. Improved therapeutic guidance has been demonstrated, showing the potential of FFRCT to streamline and rationalize the care of patients suspected of having CAD and improve outcomes while reducing overall health care costs ( 7 , 8 ). The purpose of this review is to describe the scientific principles, clinical validation, and implementation of various FFRCT approaches, their precursors, and related imaging tests.

Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve: Result From the MACHINE Consortium

Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease. Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent (R=0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; P<0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%–63%) by CTA to 78% (75%–82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%–76%) by CTA to 85% (81%–89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR. Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

CT angiography for planning transcatheter aortic valve replacement using automated tube voltage selection: Image quality and radiation exposure

PURPOSE To assess image quality and accuracy of CT angiography (CTA) for transcatheter aortic valve replacement (TAVR) planning performed with 3rd generation dual-source CT (DSCT). MATERIAL AND METHODS We evaluated 125 patients who underwent TAVR-planning CTA on 3rd generation DSCT. A two-part protocol was performed including retrospectively ECG-gated coronary CTA (CCTA) and prospectively ECG-triggered aortoiliac CTA using 60mL of contrast medium. Automated tube voltage selection and advanced iterative reconstruction were applied. Effective dose (ED), signal-to-noise (SNR) and contrast-to-noise ratios (CNR) were calculated. Five-point scales were used for subjective image quality analysis. In patients who underwent TAVR, sizing parameters were obtained. RESULTS Image quality was rated good to excellent in 97.6% of CCTA and 100% of aortoiliac CTAs. CTA studies at >100kV showed decreased objective image quality compared to 70-100kV (SNR, all p≤0.0459; CNR, all p≤0.0462). Mean ED increased continuously from 70 to >100kV (CCTA: 4.5±1.7mSv-13.6±2.9mSv, all p≤0.0233; aortoiliac CTA: 2.4±0.9mSv-6.8±2.7mSv, all p≤0.0414). In 39 patients TAVR was performed and annulus diameter was within the recommended range in all patients. No severe cardiac or vascular complications were noted. CONCLUSION 3rd generation DSCT provides diagnostic image quality in TAVR-planning CTA and facilitates reliable assessment of TAVR device and delivery option while reducing radiation dose.

CT coronary calcium scoring with tin filtration using iterative beam-hardening calcium correction reconstruction

OBJECTIVES To investigate the diagnostic accuracy of CT coronary artery calcium scoring (CACS) with tin pre-filtration (Sn100kVp) using iterative beam-hardening correction (IBHC) calcium material reconstruction compared to the standard 120kVp acquisition. BACKGROUND Third generation dual-source CT (DSCT) CACS with Sn100kVp acquisition allows significant dose reduction. However, the Sn100kVp spectrum is harder with lower contrast compared to 120kVp, resulting in lower calcium score values. Sn100kVp spectral correction using IBHC-based calcium material reconstruction may restore comparable calcium values. METHODS Image data of 62 patients (56% male, age 63.9±9.2years) who underwent a clinically-indicated CACS acquisition using the standard 120kVp protocol and an additional Sn100kVp CACS scan as part of a research study were retrospectively analyzed. Datasets of the Sn100kVp scans were reconstructed using a dedicated spectral IBHC CACS reconstruction to restore the spectral response of 120kVp spectra. Agatston scores were derived from 120kVp and IBHC reconstructed Sn100kVp studies. Pearsons correlation coefficient was assessed and Agatston score categories and percentile-based risk categorization were compared. RESULTS Median Agatston scores derived from IBHC Sn100kVp scans and 120kVp acquisition were 31.7 and 34.1, respectively (p=0.057). Pearsons correlation coefficient showed excellent correlation between the acquisitions (r=0.99, p<0.0001). Agatston score categories and percentile-based cardiac risk categories showed excellent agreement (ĸ=1.00 and ĸ=0.99), resulting in a low cardiac risk reclassification of 1.6% with the use of IBHC CACS reconstruction. Image noise was 24.9±3.6HU in IBHC Sn100kVp and 17.1±3.9HU in 120kVp scans (p<0.0001). The dose-length-product was 13.2±3.4mGycm with IBHC Sn100kVp and 59.1±22.9mGycm with 120kVp scans (p<0.0001), resulting in a significantly lower effective radiation dose (0.19±0.07mSv vs. 0.83±0.33mSv, p<0.0001) for IBHC Sn100kVp scans. CONCLUSION Low voltage CACS with tin filtration using a dedicated IBHC CACS material reconstruction algorithm shows excellent correlation and agreement with the standard 120kVp acquisition regarding Agatston score and cardiac risk categorization, while radiation dose is significantly reduced by 75% to the level of a chest x-ray.

Coronary CT Angiography–derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling

Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFRCFD) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFRML)-against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods A total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFRCFD and FFRML. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results On a per-lesion and per-patient level, FFRML showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFRCFD resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFRML and 0.89 for FFRCFD showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFRML (AUC, 0.91) and FFRCFD (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFRML was significantly shorter compared with that of FFRCFD (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042). Conclusion The FFRML algorithm performs equally in detecting lesion-specific ischemia when compared with the FFRCFD approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.

High-pitch low-voltage CT coronary artery calcium scoring with tin filtration: accuracy and radiation dose reduction

ObjectivesTo investigate diagnostic accuracy and radiation dose of high-pitch CT coronary artery calcium scoring (CACS) with tin filtration (Sn100kVp) versus standard 120kVp high-pitch acquisition.Methods78 patients (58% male, 61.5±9.1 years) were prospectively enrolled. Subjects underwent clinical 120kVp high-pitch CACS using third-generation dual-source CT followed by additional high-pitch Sn100kVp acquisition. Agatston scores, calcium volume scores, Agatston score categories, percentile-based risk categorization and radiation metrics were compared.Results61/78 patients showed coronary calcifications. Median Agatston scores were 34.9 [0.7–197.1] and 41.7 [0.7–207.2] and calcium volume scores were 34.1 [0.7–218.0] for Sn100kVp and 35.7 [1.1–221.0] for 120kVp acquisitions, respectively (both p<0.0001). Bland-Altman analysis revealed underestimated Agatston scores and calcium volume scores with Sn100kVp versus 120kVp acquisitions (mean difference: 16.4 and 11.5). However, Agatston score categories and percentile-based risk categories showed excellent agreement (ĸ=0.98 and ĸ=0.99). Image noise was 25.8±4.4HU and 16.6±2.9HU in Sn100kVp and 120kVp scans, respectively (p<0.0001). Dose-length-product was 9.9±4.8mGy*cm and 40.9±14.4mGy*cm with Sn100kVp and 120kVp scans, respectively (p<0.0001). This resulted in significant effective radiation dose reduction (0.13±0.07mSv vs. 0.57±0.2mSv, p<0.0001) for Sn100kVp acquisitions.ConclusionCACS using high-pitch low-voltage tin-filtered acquisitions demonstrates excellent agreement in Agatston score and percentile-based cardiac risk categorization with standard 120kVp high-pitch acquisitions. Furthermore, radiation dose was significantly reduced by 78% while maintaining accurate risk prediction.Key points• Coronary artery calcium scoring with tin filtration reduces radiation dose by 78%.• There is excellent correlation between high-pitch Sn100kVp and standard 120kVp acquisitions.• Excellent agreement regarding Agatston score categories and percentile-based risk categorization was achieved.• No cardiac risk reclassifications were observed using Sn100kVp coronary artery calcium scoring.

Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve Based on Machine Learning for Risk Stratification of Non-Culprit Coronary Narrowings in Patients with Acute Coronary Syndrome

This study investigated the prognostic value of coronary computed tomography angiography (cCTA)-derived fractional flow reserve (CT-FFR) in patients with acute coronary syndrome (ACS) and multivessel disease to gauge significance and guide management of non-culprit lesions. We retrospectively analyzed data of 48 patients (56 ± 10 years, 60% men) who were admitted for symptoms suggestive of ACS and underwent dual-source cCTA followed by invasive coronary angiography with culprit lesion intervention. Culprit lesions were retrospectively identified on cCTA using images obtained during invasive coronary angiography. Non-culprit lesions with ≥25% luminal stenosis and deferred intervention were evaluated using a machine learning CT-FFR algorithm to determine lesion-specific ischemia (CT-FFR ≤0.80). Follow-up was performed. CT-FFR identified lesion-specific ischemia in 23 of 81 non-culprit lesions. After a median follow-up of 19.5 months, 14 patients (29%) had major adverse cardiac events (MACE). Univariate Cox regression analysis revealed that CT-FFR ≤0.80 (hazard ratio [HR] 3.77 [95% confidence interval 1.16 to 12.29], p = 0.027), Framingham risk score (FRS) (HR 2.96 [1.01 to 7.63], p = 0.038), and a CAD-RADS classification ≥3 (HR 3.12 [1.03 to 10.17], p = 0.051) were predictors of MACE. In a risk-adjusted model controlling for FRS and CAD-RADS ≥3, CT-FFR ≤0.80 remained a predictor of MACE (1.56 [1.01 to 2.83], p = 0.048). Receiver operating characteristics analysis including FRS, CAD-RADS ≥ 3, and CT-FFR ≤0.80 (area under the curve 0.78) showed incremental discriminatory power over FRS alone (area under the curve 0.66, p = 0.032). CT-FFR of non-culprit lesions in patients with ACS and multivessel disease adds prognostic value to identify risk of future MACE.

MRI Post-Processing Methods for Myocardial Infarct Quantification

Myocardial infarct (MI) size has been increasingly used as an endpoint in multiple clinical trials and has thus become an important clinical measure. While late gadolinium enhancement MRI is considered the clinical reference standard to detect, characterize, and quantify MI, there is no established universal quantification algorithm that provides reliable MI assessment in every scenario. Efforts have been made to improve the binary threshold-based methods which dichotomize MRI voxels as either healthy or infarcted. Novel algorithms have also been proposed to quantify the actual infarcted tissue content of each MRI voxel while accounting for partial volume averaging, a common issue in quantitative MRI. Currently, the full-width at half-maximum binary algorithm seems to have the highest accuracy and reproducibility. Non-binary algorithms show comparable results; however, the literature is limited in terms of their clinical feasibility.